We conducted several experiments to test our proxy-based clustering hypothesis. It is important to note that in practice, it is not possible to determine before hand, the type and extent of non-IID imbalance that could plague a DL environment. As such, we can only simulate imbalance by introducing bias and noise into our dataset. To this end, [1]} provides a popular non-IID benchmark that broadly categorizes non-IID flavors into different sets. These include quantity-based label imbalance (#label2), distribution-based label imbalance (labeldir), quantity skew, noise-based feature imbalance (feat-noise) and mixed non-IID settings. We conduct our experiments on four non-IID cases and show which scenarios best benefit from topological pre-processing.
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